Channel Opportunity: Remedying Data Management Woes

 
 
By Michael Vizard  |  Posted 2016-05-25 Email
 
 
 
 
 
 
 
 
 
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    1 - Channel Opportunity: Remedying Data Management Woes
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    Channel Opportunity: Remedying Data Management Woes

    Solution providers can help solve data management challenges that are taxing IT organizations and have a direct impact on their revenue and costs.
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    2 - The Trouble With Data Quality
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    The Trouble With Data Quality

    40% were very confident in their organizations' data quality management (DQM) practices or the quality of data within their companies. Half indicated that the DQM practices put in place by their organizations and the quality of the data used overall were either slightly better than satisfactory, or at least good enough in general.
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    3 - Business Value of Data
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    Business Value of Data

    Just over half (51%) said data quality affects revenue, while 49% said it has an impact on costs.
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    4 - Impact of Data Quality on Business
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    Impact of Data Quality on Business

    Nearly two-thirds (65%) said that 10% to 49% of the business value can be lost due to poor data quality, while 29% said 50% or more of business value can be lost. Only 6% said that little-to-no business value is lost as a result of poor data quality. A full 85% surmise the organizations they work for think the quality of the data they have is better than it actually is.
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    5 - Growth in Data Volume
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    Growth in Data Volume

    A full 95% said they expected the number of data sources and the volumes of data in their organizations to increase in the coming year. Almost 70% expect data volumes to grow by up to 70%, while nearly 30% said they expect data volumes to increase by anywhere from 75% to nearly 300%.
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    6 - DQM Tools in Use
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    DQM Tools in Use

    57% said they use some form of big data tools, followed by master data management tools (54%) and data-cleansing tools (51%).
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    7 - Causes of Poor Data Quality
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    Causes of Poor Data Quality

    Data entry by employees (58%) was cited most often as the cause of poor quality data, followed by data migration/conversion projects (47%) and mixed entries by multiple users (44%).
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    8 - Poor Methods for Ensuing Data Quality
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    Poor Methods for Ensuing Data Quality

    Just under half (46%) said they find data errors by using reports and then taking subsequent corrective action as their means for DQM, while 37.5% employed a manual data-cleansing process. Another 9% said they avoid DQM completely.
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    9 - Limited Knowledge of Data Location
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    Limited Knowledge of Data Location

    56% were somewhat confident, unaware of or less than confident in terms of knowing whether all the data sources required for their purposes had been aggregated prior to cleansing. Less than half (43%) were very confident in their knowledge.
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    10 - Future DQM Plans
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    Future DQM Plans

    A full 65% said they are currently implementing or developing a plan. But only 24% said they implemented a plan that is actually working.
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    11 - DQM Tools and Services Needed Most
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    DQM Tools and Services Needed Most

    At 46%, big data tops the list of most needed DQM tools and services, followed by data cleansing (41%) and master data management (40%).
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    12 - Machine Learning Comes to the Fore
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    Machine Learning Comes to the Fore

    42% want to use machine learning within next 12 months, and 15% want machine learning programs in the next 24 months. Another 22% said they already had a machine-learning program. The top application for machine learning is predictive analytics (67%).
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    13 - Use Cases for Machine Learning
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    Use Cases for Machine Learning

    Asset management is the biggest use for machine learning, at 47%, followed by data discovery (45%) and decision-making (39%).
 

Data management is a ripe opportunity for solution providers throughout the channel. As the amount of data that organizations need to manage grows exponentially, the majority of IT organizations are in the midst of crafting a new data management strategy--the centerpiece of which is often a new big data initiative, according to the findings of a new survey conducted by 451 Research on behalf of Blazent, a provider of a platform for processing big data. The study, based on a poll of 200 IT leaders, finds that most of them face challenges is with the quality of the data that is collected. They indicated that data quality affects their organizations' revenue and costs. As a result, the majority of them are also looking for new data quality management (DQM) tools. The survey also suggests that most IT organizations are now making data quality a priority because they want to advance machine-learning projects that have the potential to finally turn all the data they collect into a strategic business asset. Channel Insider looks at key takeaways from the report.

 
 
 
 
 
 
 
 
 
 
 
 

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